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Social Capital and Managers’ Use of Corporate Resources

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Abstract

This study investigates how social capital affects managers’ use of corporate resources. We find that for firms located in U.S. counties with a high level of social capital, (i) corporate cash holdings have higher marginal value, (ii) the contribution of capital expenditures to shareholder value is higher, and (iii) acquirers experience higher announcement-period abnormal stock returns. We further find that social capital decreases both over- and under-investment, and thus improves ex post corporate investment efficiency. Our evidence suggests that in communities with a high level of social capital, strong social norms and dense social networks constrain unethical corporate behavior, which induces more efficient use of corporate resources.

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Data Availability

The data used in this study are available from the public sources identified in the paper.

Notes

  1. Prior studies in these disciplines have found that social capital improves governmental performance (Knack 2002; Putnam et al. 1993), promotes economic growth (Knack and Keefer 1997), reduces crime rates (Buonanno et al. 2009), and improves individuals’ career prospects (Burt 2000; Lin et al. 1981).

  2. Specifically, one dollar of cash holdings is, on average, worth less than one from shareholders’ perspective (Faulkender and Wang 2006), because shareholders anticipate managers will waste part of the cash holdings for purposes such as empire building (Dittmar and Mahrt-Smith 2007; Masulis et al. 2007). Similarly, a large number of acquisitions have near-zero or negative announcement returns, because shareholders perceive these acquisitions as value-destroying (Andrade et al. 2001; Moeller et al. 2004).

  3. In their review paper, Adler and Kwon (2002) identify 23 definitions of social capital.

  4. Consistent with these views, Hilary and Hui (2009) find that firms headquartered in a county where the level of religious participation is high tend to exhibit lower risk exposure. Dyreng et al. (2012) find that managers tend to adopt less aggressive accrual choices if the manager’s firm is headquartered in a county with a higher level of religious participation. In the U.S., Di Giuli and Kostovetsky (2014) find that firms headquartered in Democrat-leaning states have higher CSR ratings than firms headquartered in Republican-leaning states.

  5. Similar to our arguments, Hoi et al (2018) suggest that social capital constrains opportunistic managerial behavior such as CEO overcompensation. The study of Hoi et al (2018) focuses on the relation between social capital and rent extraction in CEO overcompensation, while our study focuses on how social capital affects opportunistic managerial behavior associated with corporate cash holdings, capital expenditures, and corporate acquisitions.

  6. Respn and Pvote capture the norm aspect of social capital, while Nccs and Assn capture the network aspect of social capital.

  7. Since the data are not available for every year (e.g., some of the data are obtained from the decennial survey), following Hilary and Hui (2009), values in the missing years are filled by linear interpolation.

  8. Following Masulis et al. (2007), the parameters of the model are computed over the period trading days [− 210, −11], with the CRSP value-weighted return as the market return. We require at least 60 observations to be available during the estimation period.

  9. The data are available on request from NERCRD (http://aese.psu.edu/nercrd).

  10. The descriptive statistics of the variables used in the analysis of capital expenditures are very similar to those in the analysis of the cash holdings, so, for brevity, we do not present these.

  11. Consistent with Verdi (2006), we find far more firms that under-invest (50,263 firm-year observations) than over-invest (23,147 firm-year observations), suggesting that the residuals are highly right-skewed.

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Authors and Affiliations

  1. Australian National University, Canberra, Australia

    Ziqi Gao

  2. UNSW Sydney, Sydney, Australia

    Leye Li

  3. Research School of Accounting, Australian National University, Canberra, Australia

    Louise Yi Lu

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  1. Ziqi Gao
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  2. Leye Li
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Appendix: Definitions of Variables Used

Appendix: Definitions of Variables Used

Social Capital :

is based on the social capital measure of Rupasingha et al. (2006). It is computed by using the first principal component from Respn (the response rate to the surveys conducted by the U.S. Census Bureau), Pvote (voter turnout in the U.S. presidential elections), Nccs (the number of non-governmental organizations), and Assn (the number of social associations)

Age :

is the county-level median age of the population

Income :

is the household median income in each county

Population :

is the county-level population size

Pop_Density :

is population per square mile in a county

Pop_Growth :

is the 10-year percentage change in population in a county

Married :

is the percentage of the population over 15 that is currently married in a county

Minority :

is the county-level percentage of minorities

Religious :

is the county-level percentage of religious adherents; this variable is computed following Hilary and Hui (2009)

AbRet :

is abnormal stock return over the year ending three months after the end of the year, calculated by subtracting the Fama–French book-to-market and size-matched portfolio return from the raw return

ΔCash :

is a change in the sum of cash and marketable securities, divided by the market value of equity at the year-beginning

ΔCapex :

is change in capital expenditure divided by market value of equity at the year-beginning

Analysts :

is log (1 + the number of analysts covering a firm)

Institutions :

is the percentage of firm shares held by institutional investors

Leverage :

is total debt over market value of total assets

Earnings :

is earnings before extraordinary items divided by market value of equity

ΔNCA :

is change in non-cash assets of the year divided by market value of equity at the year-beginning

ΔR&D :

is change in R&D expenses of the year divided by market value of equity at the year-beginning

ΔInterest :

is change in interest expense of the year divided by market value of equity at the year-beginning

ΔDividend :

is change in cash dividend payment of the year divided by market value of equity at the year-beginning

NetFinancing :

is calculated as equity issue minus equity repurchases plus change in long-term debt, divided by lagged market value of equity at the year-beginning

CAR :

is acquirer’s announcement-period abnormal returns. It is computed as the sum of [− 2, + 2] daily abnormal returns, where day 0 is the date of acquisition announcement. Abnormal returns are residuals from a standard market model, whose parameters are from the day − 210 to day − 11, using value-weighted return of CRSP as the market return. We require at least 60 observations to be available during this estimation period

MB :

is the ratio of the market value of total assets to book value of total assets

ROA :

is net income over book value of total assets

DealSize :

is the ratio of transaction value to acquirer’s market value

HighTech :

is an indicator variable that equals 1 if acquirer and target are both from the hightech industries, and zero otherwise

Diversifying :

is an indicator variable that equals one if acquirer and target are not in the same Fama–French 48-industry classification, and zero otherwise

Public :

is an indicator variable that equals 1 for public targets, and zero otherwise

Private :

is an indicator variable that equals 1 for private targets, and zero otherwise

Sub :

is an indicator variable that equals 1 for subsidiary targets, and zero otherwise

CashDeal :

is an indicator variable that equals 1 for purely cash-financed deals, and zero otherwise

StockDeal :

is an indicator variable that equals 1 for deals at least partially stock financed, and zero otherwise

Investment :

is the sum of capital, acquisition, and R&D (zero if missing) expenditures, minus cash receipts from sale of PPE, multiplied by 100 and divided by lagged total assets

OverInv :

is a ranked variable based on the ranked decile cash and leverage, where leverage is multiplied by − 1 before the ranking, so both cash and leverage are increasing in the likelihood of over-investment

DD_AQ :

is a measure of accrual quality calculated from the standard deviation of firm-level residuals from the Dechow and Dichev’s (2002) model during the previous five years and multiplied by − 1

CFOVol :

is the standard deviation of cash flow from operations divided by average total assets from the year t − 5 to the year t − 1

SalesVol :

is the standard deviation of sales divided by average total assets from the year t − 5 to the year t − 1

InvVol :

is the standard deviation of total investment divided by average total assets from the year t − 5 to the year t − 1

ZScore :

is the Altman Z score (Altman 1968)

Tangibility :

is the ratio of PPE to total assets

IndKStructure :

is the industry mean value of each firm’s ratio of long-term debt to the sum of long-term debt to the market value of equity

CFOSale :

is the ratio of CFO to sales

Dividend :

is an indicator variable that equals 1 if the firm paid a dividend, and zero otherwise

Firm_Age :

is the log of 1 plus the difference between the current year and the first year in which the firm appears in Compustat

OperCycle :

is the log of receivables to sales plus inventory to COGS multiplied by 360

Loss :

is an indicator variable that equals 1 if net income before extraordinary items is negative, and zero otherwise

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Gao, Z., Li, L. & Lu, L.Y. Social Capital and Managers’ Use of Corporate Resources. J Bus Ethics 168, 593–613 (2021). https://doi.org/10.1007/s10551-019-04223-7

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